The X-ray beam in most computed tomography (CT) scanners is generally polychromatic. Yet, third-generation CT scanners generate images based upon data according to the energy integration nature of the detectors. These conventional detectors are called energy-integrating detectors and acquire energy integration X-ray data. On the other hand, photon-counting detectors are configured to acquire the spectral nature of the X-ray source, rather than the energy integration nature. To obtain the spectral nature of the transmitted X-ray data, the photon-counting detectors split the X-ray beam into its component energies or spectrum bins and count the number of photons in each of the bins. The use of the spectral nature of the X-ray source in CT is often referred to as spectral CT. Since spectral CT involves the detection of transmitted X-rays at two or more energy levels, spectral CT generally includes dual-energy CT by definition.
Spectral CT is advantageous over conventional CT because spectral CT offers the additional clinical information included in the full spectrum of an X-ray beam. For example, spectral CT facilitates in discriminating tissues, differentiating between tissues containing calcium and tissues containing iodine, and enhancing the detection of smaller vessels. Among other advantages, spectral CT reduces beam-hardening artifacts, and increases accuracy in CT numbers independent of the type of scanner.
Conventional attempts include the use of integrating detectors in implementing spectral CT. One attempt includes dual sources and dual integrating detectors that are placed on the gantry at a predetermined angle with respect to each other for acquiring data as the gantry rotates around a patient. Another attempt includes the combination of a single source that performs kV-switching and a single integrating detector, which is placed on the gantry for acquiring data as the gantry rotates around a patient. Yet another attempt includes a single source and dual integrating detectors that are layered on the gantry for acquiring the data as the gantry rotates around a patient. All of these attempts at spectral CT were not successful in substantially solving issues, such as beam hardening, temporal resolution, noise, poor detector response, poor energy separation, etc., for reconstructing clinically viable images.
Iterative reconstruction (IR) can be incorporated into a CT scanner system, such as one of the CT scanners described above. IR compares a forward projection, through an image estimate, to the measured data. Differences are used to update the image estimate. Measured data includes the true system optics, which blurs the data, as well as physical effects, such as scatter and beam hardening. When the reprojected data and measured data match, a good estimate of the true solution is obtained as a reconstructed image. Conventionally, reconstruction assumed a point source, a point detector, point image voxels, and snapshot acquisition, which is called pencil beam geometry.
For low-dose applications, data fidelity implies also matching the noise, which is not desirable. Therefore, most systems use a “cost function” inserted into the iterations in order to reduce noise while maintaining true features.
System optics modeling (SOM) includes knowing (1) the extent of the source and how its emissivity varies with position, (2) the size of the detector element, (3) the relative geometry (system magnification) of the source and detector elements, (4) image voxel size and shape, and (5) the rotation of the gantry during each data sample.